{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "二项式分布就是只有两个可能结果的分布，例如：阴和阳、成功和失败、得到和丢失、赢和败，每一次尝试成功和失败的概率相等。\n",
    "\n",
    "结果有可能不一定相等。如果在实验中成功的概率为0.9，则失败的概率可以很容易地计算得到 q = 1 - 0.9 = 0.1。\n",
    "\n",
    "每一次尝试都是独立的，因为前一次的结果不能决定或影响当前的结果。\n",
    "\n",
    "只有两个可能的结果并且重复n次的实验叫做二项式。二项分布的参数是N和p，其中N是试验的总数，p是每次试验成功的概率。\n",
    "\n",
    "在上述说明的基础上，二项式分布的属性包括：\n",
    "\n",
    "- 每个试验都是独立的。\n",
    "- 在试验中只有两个可能的结果：成功或失败。\n",
    "- 总共进行了n次相同的试验。\n",
    "- 所有试验成功和失败的概率是相同的。 （试验是一样的）\n",
    "\n",
    "## 公式\n",
    "$$ Binom(k|N,p) = {N \\choose k}p^{k}(1-p)^{N-k} $$\n",
    " $N \\cdot p$ 表示分布的均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "# PMP概率质量函数\n",
    "plt.bar(x=np.arange(10),height=(stats.binom.pmf(np.arange(10), p=.5, n=10)), color=\"#008fd5\")\n",
    "# CDF累积分布函数\n",
    "plt.plot(np.arange(10),stats.binom.cdf(np.arange(10), p=.5, n=10), color=\"#fc4f30\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 不同的p值会改变PMF和CDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "# PMP概率质量函数\n",
    "plt.bar(x=np.arange(10),height=(stats.binom.pmf(np.arange(10), p=.8, n=10)), color=\"#008fd5\")\n",
    "# CDF累积分布函数\n",
    "plt.plot(np.arange(10),stats.binom.cdf(np.arange(10), p=.8, n=10), color=\"#fc4f30\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### N值越大，PMF越扁平"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "# PMP概率质量函数\n",
    "plt.plot(np.arange(10),(stats.binom.pmf(np.arange(10), p=.5, n=10)))\n",
    "\n",
    "plt.plot(np.arange(30),(stats.binom.pmf(np.arange(30), p=.5, n=30)))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
